Paper Citation Record · LEDGER

How Should We Extract Discrete Audio Tokens from Self-Supervised Models?

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.10735.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2406.10735 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:56:42.828585Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T16:08:37.447210Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e0818596-5c7a-420f-b388-baf295849936 · inbound

Scaling Transformers for Low-Bitrate High-Quality Speech Coding cites this paper.

Scaling Transformers for Low-Bitrate High-Quality Speech Coding How Should We Extract Discrete Audio Tokens from Self-Supervised Models?

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T05:56:42.828585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dcb80e2-5104-49ea-bc20-9dc7a316d8d6 · inbound

Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs cites this paper.

Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs How Should We Extract Discrete Audio Tokens from Self-Supervised Models?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T16:46:49.201803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4fe5b26-44e0-4c80-b291-bd45e6787f1e · inbound

Self-Guidance: Enhancing Neural Codecs via Decoder Manifold Alignment cites this paper.

Self-Guidance: Enhancing Neural Codecs via Decoder Manifold Alignment How Should We Extract Discrete Audio Tokens from Self-Supervised Models?

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:08:37.448943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9305c4ad-b73a-4cd5-abea-2a92cc103243 · inbound

Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens cites this paper.

Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens How Should We Extract Discrete Audio Tokens from Self-Supervised Models?

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-03T08:35:53.725939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.